
"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFXLMRobertaModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 	__a	(   unittest.TestCase						):






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"      )

              UpperCamelCase       			=		{
                  "input_ids": tf.convert_to_tensor([[0, 2_646, 10_269, 83, 99_942, 2]]							,   dtype=tf.intaa      ),  # "My dog is cute"
                  "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]							,   dtype=tf.intaa      ),
              }

              UpperCamelCase       			=		model(UpperCAmelCase_      )["last_hidden_state"]
              UpperCamelCase       			=		tf.TensorShape((1, 6, 768)      )
              self.assertEqual(output.shape							,   UpperCAmelCase_      )
              # compare the actual values for a slice.
              UpperCamelCase       			=		tf.convert_to_tensor(
                  [
                      [
                          [0.0681762, 0.10894451, 0.06772504],
                          [-0.06423668, 0.02366615, 0.04329344],
                          [-0.06057295, 0.09974135, -0.00070584],
                      ]
                  ]							,   dtype=tf.floataa							,   )

              self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,   expected_slice.numpy()							,   atol=1e-4      )      )







"""simple docstring"""


import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  _lowerCAmelCase						):






       @register_to_config
       def __init__(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				float							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str							,   UpperCAmelCase_			:				bool = False							,   )->	str:







              """simple docstring"""
              super().__init__()

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		nn.Embedding(UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		False

              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )

              UpperCamelCase       			=		TaConfig(
                  vocab_size=UpperCAmelCase_							,   d_model=UpperCAmelCase_							,   num_heads=UpperCAmelCase_							,   d_kv=UpperCAmelCase_							,   d_ff=UpperCAmelCase_							,   dropout_rate=UpperCAmelCase_							,   feed_forward_proj=UpperCAmelCase_							,   is_decoder=UpperCAmelCase_							,   is_encoder_decoder=UpperCAmelCase_							,   )

              UpperCamelCase       			=		nn.ModuleList()
              for lyr_num in range(UpperCAmelCase_      ):
                     UpperCamelCase       			=		TaBlock(UpperCAmelCase_      )
                     self.encoders.append(UpperCAmelCase_      )

              UpperCamelCase       			=		TaLayerNorm(UpperCAmelCase_      )
              UpperCamelCase       			=		nn.Dropout(p=UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				int							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				str      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.token_embedder(UpperCAmelCase_      )

              UpperCamelCase       			=		encoder_input_tokens.shape[1]
              UpperCamelCase       			=		torch.arange(UpperCAmelCase_							,   device=encoder_input_tokens.device      )
              x += self.position_encoding(UpperCAmelCase_      )

              UpperCamelCase       			=		self.dropout_pre(UpperCAmelCase_      )

              # inverted the attention mask
              UpperCamelCase       			=		encoder_input_tokens.size()
              UpperCamelCase       			=		self.get_extended_attention_mask(UpperCAmelCase_							,   UpperCAmelCase_      )

              for lyr in self.encoders:
                     UpperCamelCase       			=		lyr(UpperCAmelCase_							,   UpperCAmelCase_      )[0]
              UpperCamelCase       			=		self.layer_norm(UpperCAmelCase_      )

              return self.dropout_post(UpperCAmelCase_      ), encoder_inputs_mask





